# Table S2
# =======================================================================================

# Disgust
# =====================
mDisgust1 <- felm(disgust ~ fem_r | as.factor(manuscript_id), data = tomodel_text, weights = tomodel_text$weight_n)

mDisgust2 <- felm(disgust ~ (fem_r*gender_type3)-gender_type3 | as.factor(manuscript_id), data = tomodel_text, weights = tomodel_text$weight_n)


# Negative
# =====================
mNegative1 <- felm(negative ~ fem_r | as.factor(manuscript_id), data = tomodel_text, weights = tomodel_text$weight_n)

mNegative2 <- felm(negative ~ (fem_r*gender_type3)-gender_type3 | as.factor(manuscript_id), data = tomodel_text, weights = tomodel_text$weight_n)

# Positive
# =====================
mPositive1 <- felm(positive ~ fem_r | as.factor(manuscript_id), data = tomodel_text, weights = tomodel_text$weight_n)

mPositive2 <- felm(positive ~ (fem_r*gender_type3)-gender_type3 | as.factor(manuscript_id), data = tomodel_text, weights = tomodel_text$weight_n)

# Table S2
# =====================
texreg(list(mNegative1, mNegative2, mPositive1, mPositive2, mDisgust1, mDisgust2)
       , custom.model.names = c("Negativity I ", "Negativity II"
                                , "Positivity I", "Positivity II"
                                , "Disgust I", "Disgust II")
       , custom.coef.names = c("Female Reviewer"
                               , "Male * Female Reviewer", "Female * Female Reviewer")
       , sideways = TRUE
       , caption.above = TRUE
       , caption = "OLS Regression Results: Tone in review reports conditional on reviewer gender and authorship type"
       , file = here("02_output", "01_tables", "table_s2.tex")
)
